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REMLA  

Robust Expectation-Maximization Estimation for Latent Variable Models
View on CRAN: Click here


Download and install REMLA package within the R console
Install from CRAN:
install.packages("REMLA")

Install from Github:
library("remotes")
install_github("cran/REMLA")

Install by package version:
library("remotes")
install_version("REMLA", "1.1")



Attach the package and use:
library("REMLA")
Maintained by
Bryan Ortiz-Torres
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-03-26
Latest Update: 2024-03-26
Description:
Traditional latent variable models assume that the population is homogeneous, meaning that all individuals in the population are assumed to have the same latent structure. However, this assumption is often violated in practice given that individuals may differ in their age, gender, socioeconomic status, and other factors that can affect their latent structure. The robust expectation maximization (REM) algorithm is a statistical method for estimating the parameters of a latent variable model in the presence of population heterogeneity as recommended by Nieser & Cochran (2023) . The REM algorithm is based on the expectation-maximization (EM) algorithm, but it allows for the case when all the data are generated by the assumed data generating model.
How to cite:
Bryan Ortiz-Torres (2024). REMLA: Robust Expectation-Maximization Estimation for Latent Variable Models. R package version 1.1, https://cran.r-project.org/web/packages/REMLA. Accessed 21 Nov. 2024.
Previous versions and publish date:
1.0 (2024-03-26 10:20)
Other packages that cited REMLA R package
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Complete documentation for REMLA
Functions, R codes and Examples using the REMLA R package
Full REMLA package functions and examples
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